Patrick Anderson
- Published in print:
- 2013
- Published Online:
- September 2013
- ISBN:
- 9780804758307
- eISBN:
- 9780804783224
- Item type:
- book
- Publisher:
- Stanford University Press
- DOI:
- 10.11126/stanford/9780804758307.001.0001
- Subject:
- Economics and Finance, Financial Economics
For decades, the traditional approaches to business valuation (market, asset, and income) have taken center stage in the assessment of the firm. This book presents an expanded valuation toolkit, ...
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For decades, the traditional approaches to business valuation (market, asset, and income) have taken center stage in the assessment of the firm. This book presents an expanded valuation toolkit, consisting of nine well-defined valuation principles hailing from the fields of economics, finance, accounting, taxation, and management. It ultimately argues that the “value functional” approach to business valuation avoids most of the shortcomings of its competitors, and more correctly matches the actual motivations and information held by stakeholders. To remedy the shortcomings of existing theory, the author proposes a new definition of the firm that is consistent with the principle that entrepreneurs maximize value, not profit.Less
For decades, the traditional approaches to business valuation (market, asset, and income) have taken center stage in the assessment of the firm. This book presents an expanded valuation toolkit, consisting of nine well-defined valuation principles hailing from the fields of economics, finance, accounting, taxation, and management. It ultimately argues that the “value functional” approach to business valuation avoids most of the shortcomings of its competitors, and more correctly matches the actual motivations and information held by stakeholders. To remedy the shortcomings of existing theory, the author proposes a new definition of the firm that is consistent with the principle that entrepreneurs maximize value, not profit.
Jonathan Fletcher
- Published in print:
- 2015
- Published Online:
- November 2015
- ISBN:
- 9780190207434
- eISBN:
- 9780190207465
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190207434.003.0020
- Subject:
- Economics and Finance, Financial Economics
Some view performance measures based on the stochastic discount factor approach as having a stronger theoretical basis than traditional performance measures. The stochastic discount factor approach ...
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Some view performance measures based on the stochastic discount factor approach as having a stronger theoretical basis than traditional performance measures. The stochastic discount factor approach can be used to evaluate mutual fund performance using linear factor models, nonlinear models, and measures based on weaker economic restrictions than required by a full asset pricing model. The stochastic discount factor approach can address investor heterogeneity, as investors can evaluate the value added by a fund differently from one another. This chapter provides an overview of the stochastic discount factor approach to evaluate mutual fund performance. The chapter discusses alternative approaches used to construct stochastic discount factors to evaluate fund performance and reviews empirical evidence on mutual fund performance using the stochastic discount factor approach.Less
Some view performance measures based on the stochastic discount factor approach as having a stronger theoretical basis than traditional performance measures. The stochastic discount factor approach can be used to evaluate mutual fund performance using linear factor models, nonlinear models, and measures based on weaker economic restrictions than required by a full asset pricing model. The stochastic discount factor approach can address investor heterogeneity, as investors can evaluate the value added by a fund differently from one another. This chapter provides an overview of the stochastic discount factor approach to evaluate mutual fund performance. The chapter discusses alternative approaches used to construct stochastic discount factors to evaluate fund performance and reviews empirical evidence on mutual fund performance using the stochastic discount factor approach.
Patrick L. Anderson
- Published in print:
- 2013
- Published Online:
- September 2013
- ISBN:
- 9780804758307
- eISBN:
- 9780804783224
- Item type:
- chapter
- Publisher:
- Stanford University Press
- DOI:
- 10.11126/stanford/9780804758307.003.0009
- Subject:
- Economics and Finance, Financial Economics
The author introduces the “recursive” model that has emerged within micro-economics over the past few decades. This modern recursive equilibrium model is contrasted with the neoclassical model, in ...
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The author introduces the “recursive” model that has emerged within micro-economics over the past few decades. This modern recursive equilibrium model is contrasted with the neoclassical model, in terms of the optimization and time periods involved. The modern, multi-period consumer savings problem is introduced, as well as the “cake eating” problem and basic pricing equation. The author argues these form the basis of a modern microeconomic theory, and that the stochastic discount factor that emerges from the basic pricing equation provides a valuable insight that is lacking in the neoclassical and classical worlds. As with other valuation principles, the author tests the principle as a practical valuation tool for three actual businesses, demonstrating that is provides an incomplete basis for valuation of private firms.Less
The author introduces the “recursive” model that has emerged within micro-economics over the past few decades. This modern recursive equilibrium model is contrasted with the neoclassical model, in terms of the optimization and time periods involved. The modern, multi-period consumer savings problem is introduced, as well as the “cake eating” problem and basic pricing equation. The author argues these form the basis of a modern microeconomic theory, and that the stochastic discount factor that emerges from the basic pricing equation provides a valuable insight that is lacking in the neoclassical and classical worlds. As with other valuation principles, the author tests the principle as a practical valuation tool for three actual businesses, demonstrating that is provides an incomplete basis for valuation of private firms.
Nikolay Gospodinov and Cesare Robotti
- Published in print:
- 2013
- Published Online:
- May 2013
- ISBN:
- 9780199829699
- eISBN:
- 9780199979790
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199829699.003.0003
- Subject:
- Economics and Finance, Financial Economics
An important but still partially unanswered question in the investment field is why different assets earn substantially different returns on average. Financial economists have typically addressed ...
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An important but still partially unanswered question in the investment field is why different assets earn substantially different returns on average. Financial economists have typically addressed this question in the context of theoretically or empirically motivated asset pricing models. Since many of the proposed “risk” theories are plausible, a common practice in the literature is to take the models to the data and perform “horse races” among competing asset pricing specifications. A “good” asset pricing model should produce small pricing (expected return) errors on a set of test assets and should deliver reasonable estimates of the underlying market and economic risk premia. This chapter provides an up-to-date review of the statistical methods that are typically used to estimate, evaluate, and compare competing asset pricing models. The analysis also highlights several pitfalls in the current econometric practice and offers suggestions for improving empirical tests. Examples of topics include stochastic discount factor, two-pass cross-sectional regressions, misspecified models, and useless factors.Less
An important but still partially unanswered question in the investment field is why different assets earn substantially different returns on average. Financial economists have typically addressed this question in the context of theoretically or empirically motivated asset pricing models. Since many of the proposed “risk” theories are plausible, a common practice in the literature is to take the models to the data and perform “horse races” among competing asset pricing specifications. A “good” asset pricing model should produce small pricing (expected return) errors on a set of test assets and should deliver reasonable estimates of the underlying market and economic risk premia. This chapter provides an up-to-date review of the statistical methods that are typically used to estimate, evaluate, and compare competing asset pricing models. The analysis also highlights several pitfalls in the current econometric practice and offers suggestions for improving empirical tests. Examples of topics include stochastic discount factor, two-pass cross-sectional regressions, misspecified models, and useless factors.
Kerry E. Back
- Published in print:
- 2017
- Published Online:
- May 2017
- ISBN:
- 9780190241148
- eISBN:
- 9780190241179
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190241148.001.0001
- Subject:
- Economics and Finance, Financial Economics
This book is intended as a textbook for asset pricing theory courses at the Ph.D. or Masters in Quantitative Finance level and as a reference for financial researchers. The first two parts of the ...
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This book is intended as a textbook for asset pricing theory courses at the Ph.D. or Masters in Quantitative Finance level and as a reference for financial researchers. The first two parts of the book explain portfolio choice and asset pricing theory in single‐period, discrete‐time, and continuous‐time models. For valuation, the focus throughout is on stochastic discount factors and their properties. Traditional factor models, including the CAPM, are related to or derived from stochastic discount factors. A chapter on stochastic calculus provides the needed tools for analyzing continuous‐time models. A chapter on “ex‐plaining puzzles” and the last two parts of the book provide introductions to a number of current topics in asset pricing research, including rare disasters, long‐run risks, external and internal habits, real options, corporate financing options, asymmetric and incomplete information, heterogeneous beliefs, and non‐expected‐utility preferences. Each chapter includes a “Notes and References” section and exercises for students.Less
This book is intended as a textbook for asset pricing theory courses at the Ph.D. or Masters in Quantitative Finance level and as a reference for financial researchers. The first two parts of the book explain portfolio choice and asset pricing theory in single‐period, discrete‐time, and continuous‐time models. For valuation, the focus throughout is on stochastic discount factors and their properties. Traditional factor models, including the CAPM, are related to or derived from stochastic discount factors. A chapter on stochastic calculus provides the needed tools for analyzing continuous‐time models. A chapter on “ex‐plaining puzzles” and the last two parts of the book provide introductions to a number of current topics in asset pricing research, including rare disasters, long‐run risks, external and internal habits, real options, corporate financing options, asymmetric and incomplete information, heterogeneous beliefs, and non‐expected‐utility preferences. Each chapter includes a “Notes and References” section and exercises for students.
Andrew Ang
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199959327
- eISBN:
- 9780199382323
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199959327.003.0006
- Subject:
- Economics and Finance, Financial Economics
Assets earn risk premiums because they are exposed to underlying factor risks. The capital asset pricing model (CAPM), the first theory of factor risk, states that assets that crash when the market ...
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Assets earn risk premiums because they are exposed to underlying factor risks. The capital asset pricing model (CAPM), the first theory of factor risk, states that assets that crash when the market loses money are risky and therefore must reward their holders with high risk premiums. While the CAPM defines bad times as times of low market returns, multifactor models capture multiple definitions of bad times across many factors and states of nature.Less
Assets earn risk premiums because they are exposed to underlying factor risks. The capital asset pricing model (CAPM), the first theory of factor risk, states that assets that crash when the market loses money are risky and therefore must reward their holders with high risk premiums. While the CAPM defines bad times as times of low market returns, multifactor models capture multiple definitions of bad times across many factors and states of nature.
Kerry E. Back
- Published in print:
- 2017
- Published Online:
- May 2017
- ISBN:
- 9780190241148
- eISBN:
- 9780190241179
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190241148.003.0013
- Subject:
- Economics and Finance, Financial Economics
A continuous‐time model of a securities market is introduced. The intertemporal budget constraint is defined. SDF processes and prices of risks are defined and characterized. Many properties of SDF ...
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A continuous‐time model of a securities market is introduced. The intertemporal budget constraint is defined. SDF processes and prices of risks are defined and characterized. Many properties of SDF process are analogous to those in a single‐period model, including the relation to the risk‐free rate, orthogonal projections, the Hansen‐Jagannathan bound, and factor pricing. To value future cash flows using an SDF process, we need to assume a local martingale is a martingale. Sufficient conditions including Novikov’s condition are discussed. Use of the martingale representation theorem in a complete market to derive a portfolio that replicates a payoff is explained. A Markovian model is introduced, in which the investment opportunity set depends on state variables that form a Markov process.Less
A continuous‐time model of a securities market is introduced. The intertemporal budget constraint is defined. SDF processes and prices of risks are defined and characterized. Many properties of SDF process are analogous to those in a single‐period model, including the relation to the risk‐free rate, orthogonal projections, the Hansen‐Jagannathan bound, and factor pricing. To value future cash flows using an SDF process, we need to assume a local martingale is a martingale. Sufficient conditions including Novikov’s condition are discussed. Use of the martingale representation theorem in a complete market to derive a portfolio that replicates a payoff is explained. A Markovian model is introduced, in which the investment opportunity set depends on state variables that form a Markov process.
Kerry E. Back
- Published in print:
- 2017
- Published Online:
- May 2017
- ISBN:
- 9780190241148
- eISBN:
- 9780190241179
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190241148.003.0003
- Subject:
- Economics and Finance, Financial Economics
SDFs are defined. The first order condition for portfolio choice is interpreted as: an investor’s marginal rate of substitution is an SDF. There is a strictly positive SDF if and only if there are no ...
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SDFs are defined. The first order condition for portfolio choice is interpreted as: an investor’s marginal rate of substitution is an SDF. There is a strictly positive SDF if and only if there are no arbitrage opportunities, and there is some SDF if and only if the law of one price holds. There is a unique SDF if and only if the market is complete. Orthogonal projections are defined. There is a unique SDF in the span of the assets, and it equals the projection of any SDF onto the span of the assets. The Hansen‐Jagannathan bounds are derived. Orthogonal projections are used to show how an investor with quadratic utility hedges labor income risk.Less
SDFs are defined. The first order condition for portfolio choice is interpreted as: an investor’s marginal rate of substitution is an SDF. There is a strictly positive SDF if and only if there are no arbitrage opportunities, and there is some SDF if and only if the law of one price holds. There is a unique SDF if and only if the market is complete. Orthogonal projections are defined. There is a unique SDF in the span of the assets, and it equals the projection of any SDF onto the span of the assets. The Hansen‐Jagannathan bounds are derived. Orthogonal projections are used to show how an investor with quadratic utility hedges labor income risk.
Harold L. Cole
- Published in print:
- 2019
- Published Online:
- May 2019
- ISBN:
- 9780190941697
- eISBN:
- 9780190949068
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190941697.003.0006
- Subject:
- Economics and Finance, Macro- and Monetary Economics, Financial Economics
This chapter develops a arbitrage-based pricing model in which the extent to which the stochastic discount factor varies is unrestricted.
This chapter develops a arbitrage-based pricing model in which the extent to which the stochastic discount factor varies is unrestricted.
Kerry E. Back
- Published in print:
- 2017
- Published Online:
- May 2017
- ISBN:
- 9780190241148
- eISBN:
- 9780190241179
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190241148.003.0008
- Subject:
- Economics and Finance, Financial Economics
The dynamic model with time‐additive utility is defined. The intertemporal budget constraint is explained. SDF processes are defined in terms of a martingale property. There is a strictly positive ...
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The dynamic model with time‐additive utility is defined. The intertemporal budget constraint is explained. SDF processes are defined in terms of a martingale property. There is a strictly positive SDF process if and only if there are no arbitrage opportunities. Dynamic complete markets are explained. The difference between the price of an asset and its value calculated from an SDF process is called a bubble. There is no bubble if a transversality condition is satisfied. Some constraints on trading strategies are needed to rule out Ponzi schemes. SDF processes are derived for nominal asset prices and for asset prices denominated in a foreign currency.Less
The dynamic model with time‐additive utility is defined. The intertemporal budget constraint is explained. SDF processes are defined in terms of a martingale property. There is a strictly positive SDF process if and only if there are no arbitrage opportunities. Dynamic complete markets are explained. The difference between the price of an asset and its value calculated from an SDF process is called a bubble. There is no bubble if a transversality condition is satisfied. Some constraints on trading strategies are needed to rule out Ponzi schemes. SDF processes are derived for nominal asset prices and for asset prices denominated in a foreign currency.
Kerry E. Back
- Published in print:
- 2017
- Published Online:
- May 2017
- ISBN:
- 9780190241148
- eISBN:
- 9780190241179
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780190241148.003.0006
- Subject:
- Economics and Finance, Financial Economics
The CAPM and factor models in general are explained. Factors can be replaced by the returns or excess returns that are maximally correlated (the projections of the factors). A factor model is ...
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The CAPM and factor models in general are explained. Factors can be replaced by the returns or excess returns that are maximally correlated (the projections of the factors). A factor model is equivalent to an affine representation of an SDF and to spanning a return on the mean‐variance frontier. The use of alphas for performance evaluation is explained. Statistical factor models are defined as models in which factors explain the covariance matrix of returns. A proof is given of the Arbitrage Pricing Theory, which states that statistical factors are approximate pricing factors. The CAPM and the Fama‐French‐Carhart model are evaluated relative to portfolios based on sorts on size, book‐to‐market, and momentum.Less
The CAPM and factor models in general are explained. Factors can be replaced by the returns or excess returns that are maximally correlated (the projections of the factors). A factor model is equivalent to an affine representation of an SDF and to spanning a return on the mean‐variance frontier. The use of alphas for performance evaluation is explained. Statistical factor models are defined as models in which factors explain the covariance matrix of returns. A proof is given of the Arbitrage Pricing Theory, which states that statistical factors are approximate pricing factors. The CAPM and the Fama‐French‐Carhart model are evaluated relative to portfolios based on sorts on size, book‐to‐market, and momentum.
